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Introduction
Figure 2: Schematic illustration of the circle estimation process.
Figure 1: Schematic overview of the proposed approach
This work proposes a novel tree detection methodology, named DTCD (Distance Transform Circle Detection), based on a fast circle detection method via Distance Transform and Akaike Information Criterion (AIC) optimization [1]. More specifically, a visible-band vegetation index (RGBVI) is calculated to enhance canopy regions, followed by morphological filtering to delineate individual tree crowns. The Euclidean Distance Transform is then applied, and the local maxima of the smoothed distance map are extracted as candidate tree locations. The final detections are iteratively refined using the AIC to optimize the number of trees with respect to canopy coverage efficiency. Additionally, this work introduces a modified tree detection algorithm using point clouds, improving the detection accuracy.
Experiments - Downloads of DTCD
You can download the matlab code of the DTCD method proposed in [1]
You can download the AgiosNikolaos-3 dataset proposed in [1] from
Experimental results of DTCD on Acacia-6 dataset
Experimental results of DTCD on AgiosNikolaos-3 dataset
See the corresponding readme.txt files for more details.
Related Publications
[1] S. Markaki, C. Panagiotakis, Unsupervised Tree Detection from UAV Imagery and 3D Point Clouds via Distance Transform-based Circle Estimation and AIC Optimization, Preprint, 2025.
S. Markaki, C. Panagiotakis, Unsupervised Tree Detection from UAV Imagery and 3D Point Clouds via Distance Transform-based Circle Estimation and AIC Optimization, Remote Sensing, 2025 (under review).
[2] S. Markaki, C. Panagiotakis, Unsupervised Tree Detection and Counting via Region-based Circle Fitting, International Conference on Pattern Recognition Applications and Methods (ICPRAM), 2023.
[3] C. Panagiotakis and A. Argyros, Parameter-free Modelling of 2D Shapes with Ellipses, Pattern Recognition, vol. 53, pp. 259-275, 2016.
[4] Tong, P., Han, P., Li, S., Li, N., Bu, S., Li, Q., and Li, K. (2021). Counting trees with point-wise supervised segmentation network. Engineering Applications of
Artificial Intelligence, 100:104172
[5] C. Panagiotakis and A. Argyros, Cell Segmentation via Region-based Ellipse Fitting, IEEE International Conference on Image Processing, 2018.
[6] C. Panagiotakis and A.A. Argyros, Region-based Fitting of Overlapping Ellipses and its Application to Cells Segmentation, Image and Vision Computing, Elsevier, vol. 93, pp. 103810, 2020.
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